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Lookup NU author(s): Dr Noura Al Moubayed,
Dr Stephen McGough,
Dr Bashar Awwad Shiekh Hasan
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The article presents a discriminative approach to complement the unsupervised probabilistic nature of topic modelling. The framework transforms the probabilities of the topics per document into class-dependent deep learning models that extract highly discriminatory features suitable for classification. The framework is then used for sentiment analysis with minimum feature engineering. The approach transforms the sentiment analysis problem from the word/document domain to the topics domain making it more robust to noise and incorporating complex contextual information that are not represented otherwise. A stacked denoising autoencoder (SDA) is then used to model the complex relationship among the topics per sentiment with minimum assumptions. To achieve this, a distinct topic model and SDA per sentiment polarity is built with an additional decision layer for classification. The framework is tested on a comprehensive collection of benchmark datasets that vary in sample size, class bias and classification task. A significant improvement to the state of the art is achieved without the need for a sentiment lexica or over-engineered features. A further analysis is carried out to explain the observed improvement in accuracy.
Author(s): Al Moubayed N, McGough S, Awwad Shiekh Hasan B
Publication type: Article
Publication status: Published
Journal: PeerJ Computer Science
Print publication date: 27/01/2020
Online publication date: 27/01/2020
Acceptance date: 23/12/2019
Date deposited: 03/02/2020
ISSN (electronic): 2376-5992
Publisher: PeerJ, Ltd.
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